Hi Ray, really excellent stuff you've done here regarding correct use of flats.
I just want to make some comments regarding the use of the median vs the mean.
If we have 10 subs and for a given pixel the ADU values are:
sub 1 = 13212
sub 2 = 15234
sub 3 = 12424
sub 4 = 14243
sub 5 = 14234
sub 6 = 14700
sub 7 = 14532
sub 8 = 0
sub 9 = 12430
sub 10 =0
The mean is 11100.9 and the median is 13723.0.
The median is the value that has an equal number of smaller and larger values flanking it. Because we have an even number of subs, the median value is actually 13212 + 14234 / 2 = 13723.
So, which one of these is more "accurate". The median is in this case because the pixel values of 0 are clearly rubbish. The mean is said in this case to be "biased". The median is said to be "robust" to outliers - that's why people use it.
Also note that if sub 1 had a value of 13213 then the mean would be 11101.0 and the median would be 13723.5. The median can be fractional.
Edit: I should add that the "sample mean" and the "sample median" (what you're calculating) are both estimations of the "population mean", which the average is you took an infinite number of subs. If that were the case the number of outliers you had would be irrelevant because they would be overwhelmed by true signal, and in which case your mean and median would be exactly equal.
Hope this is helpful,
Cam
Quote:
Originally Posted by Shiraz
Hi Ken.
As I understand it, median combine will pull out the single value that is the median of all of the inputs - it cannot have a fractional ADU value, so the scaling factor will be subject to quantisation noise.
For SNR, the basic question is "of what". To fix a consistent reference for the modelling and measurements, I measured the average signal of a dim part of the galaxy to be 1/10 the sky background on one of the lights. Since the sky and the galaxy did not change much during the imaging run, I thereafter used 1/10 sky as the Signal and so the SNR values refer to the chosen part of the galaxy. I could choose some other reference point and all of the SNR results would be scaled, but the same picture would emerge when comparing model and test data. With a dim target, the noise is totally dominated by the sky, so I measured the RMS variability of a featureless part of the sky to find the noise. I turned off all of the smart bits while stacking in PI (to get an undoctored result) and used the pixel stats function of Nebulosity to do the measurements.
Interesting observation on the 314. I would guess that the Sony chip in the Atik has very low fixed pattern variability, so flats will not help unless you expose for very long periods of time.
Regards Ray
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